Bayesian Boosting (AI Studio Core)

Description

The Bayesian Boosting operator is a nested operator i.e. it has a subprocess. The subprocess must have a learner i.e. an operator that expects an ExampleSet and generates a model. This operator tries to build a better model using the learner provided in its subprocess. You need to have a basic understanding of subprocesses in order to apply this operator. Please study the documentation of the Subprocess operator for basic understanding of subprocesses.

This operator trains an ensemble of classifiers for boolean target attributes. In each iteration the training set is reweighted, so that previously discovered patterns and other kinds of prior knowledge are 'sampled out'. An inner classifier, typically a rule or decision tree induction algorithm, is sequentially applied several times, and the models are combined to a single global model. The maximum number of models to be trained are specified by the iterations parameter.

If the rescale label priors parameter is set to true, then the ExampleSet is reweighted, so that all classes are equally probable (or frequent). For two-class problems this turns the problem of fitting models to maximize weighted relative accuracy into the more common task of classifier induction. Applying a rule induction algorithm as an inner learner allows to do subgroup discovery. This option is also recommended for data sets with class skew, if a very weak learner like a decision stump is used. If the rescale label priors parameter is not set, then the operator performs boosting based on probability estimates.

If the allow marginal skews parameter is not set, then the support of each subset defined in terms of common base model predictions does not change from one iteration to the next. Analogously the class priors do not change. This is the procedure originally described in 'Scholz/2005b' in the context of subgroup discovery. Setting the allow marginal skews option to true leads to a procedure that changes the marginal weights/probabilities of subsets, if this is beneficial in a boosting context, and stratifies the two classes to be equally likely. As for AdaBoost, the total weight upper-bounds the training error in this case. This bound is reduced more quickly by the Bayesian Boosting operator.

To reproduce the sequential sampling, or knowledge-based sampling, from 'Scholz/2005b' for subgroup discovery, two of the default parameter settings of this operator have to be changed: rescale label priors must be set to true, and allow marginal skews must be set to false. In addition, a boolean (binomial) label has to be used.

This operator requires an ExampleSet as its input. To sample out prior knowledge of a different form it is possible to provide another model as an optional additional input. The predictions of this model are used to produce an initial weighting of the training set. The output of the operator is a classification model applicable for estimating conditional class probabilities or for plain crisp classification. It contains up to the specified number of inner base models. In the case of an optional initial model, this model will also be stored in the output model, in order to produce the same initial weighting during model application.

Ensemble Theory Boosting is an ensemble method, therefore an overview of the Ensemble Theory has been discussed here. Ensemble methods use multiple models to obtain better predictive performance than could be obtained from any of the constituent models. In other words, an ensemble is a technique for combining many weak learners in an attempt to produce a strong learner. Evaluating the prediction of an ensemble typically requires more computation than evaluating the prediction of a single model, so ensembles may be thought of as a way to compensate for poor learning algorithms by performing a lot of extra computation.

An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built. Thus, ensembles can be shown to have more flexibility in the functions they can represent. This flexibility can, in theory, enable them to over-fit the training data more than a single model would, but in practice, some ensemble techniques (especially bagging) tend to reduce problems related to over-fitting of the training data.

Empirically, ensembles tend to yield better results when there is a significant diversity among the models. Many ensemble methods, therefore, seek to promote diversity among the models they combine. Although perhaps non-intuitive, more random algorithms (like random decision trees) can be used to produce a stronger ensemble than very deliberate algorithms (like entropy-reducing decision trees). Using a variety of strong learning algorithms, however, has been shown to be more effective than using techniques that attempt to dumb-down the models in order to promote diversity.